Overview

Dataset statistics

Number of variables16
Number of observations772
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.7 KiB
Average record size in memory436.0 B

Variable types

NUM11
CAT5

Warnings

ACTUAL_WORTH is highly correlated with tree_3High correlation
tree_3 is highly correlated with ACTUAL_WORTHHigh correlation
PROJECT_NAME_EN is highly correlated with STATUS_CODE and 1 other fieldsHigh correlation
STATUS_CODE is highly correlated with PROJECT_NAME_ENHigh correlation
MASTER_PROJECT_EN is highly correlated with AREA_NAME_EN and 1 other fieldsHigh correlation
AREA_NAME_EN is highly correlated with MASTER_PROJECT_ENHigh correlation
PROCEDURE_AREA is highly skewed (γ1 = 27.2987483) Skewed
CURRENT_STATUS_year_after_min_year has 171 (22.2%) zeros Zeros
INSTANCE_DATE_year_after_min_year has 55 (7.1%) zeros Zeros
CREATION_year_after_min_year has 32 (4.1%) zeros Zeros

Reproduction

Analysis started2020-11-23 10:00:18.848656
Analysis finished2020-11-23 10:00:35.806450
Duration16.96 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CURRENT_STATUS_year_after_min_year
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.864313472
Minimum0
Maximum16
Zeros171
Zeros (%)22.2%
Memory size6.2 KiB
2020-11-23T15:00:35.870017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q37
95-th percentile12.3375
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.505017589
Coefficient of variation (CV)0.5976859193
Kurtosis0.1039408593
Mean5.864313472
Median Absolute Deviation (MAD)0
Skewness-0.3891867205
Sum4527.25
Variance12.2851483
MonotocityNot monotonic
2020-11-23T15:00:35.961963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
745358.7%
 
017122.2%
 
6.75364.7%
 
6303.9%
 
12.75293.8%
 
9.5141.8%
 
11.25121.6%
 
1550.6%
 
6.540.5%
 
1140.5%
 
1630.4%
 
7.7520.3%
 
1020.3%
 
1310.1%
 
1210.1%
 
3.2510.1%
 
910.1%
 
1410.1%
 
510.1%
 
10.7510.1%
 
ValueCountFrequency (%) 
017122.2%
 
3.2510.1%
 
510.1%
 
6303.9%
 
6.540.5%
 
6.75364.7%
 
745358.7%
 
7.7520.3%
 
910.1%
 
9.5141.8%
 
ValueCountFrequency (%) 
1630.4%
 
1550.6%
 
1410.1%
 
1310.1%
 
12.75293.8%
 
1210.1%
 
11.25121.6%
 
1140.5%
 
10.7510.1%
 
1020.3%
 

SEPARATED_REFERENCE
Real number (ℝ)

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.380588217e-16
Minimum-0.3201492815
Maximum3.550337086
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:36.054424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.3201492815
5-th percentile-0.3201492815
Q1-0.3201492815
median-0.2648176994
Q3-0.2446337362
95-th percentile3.550337086
Maximum3.550337086
Range3.870486368
Interquartile range (IQR)0.07551554528

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)7.243289401e+15
Kurtosis8.654853986
Mean1.380588217e-16
Median Absolute Deviation (MAD)0.05533158209
Skewness3.252682151
Sum1.065814104e-13
Variance1
MonotocityNot monotonic
2020-11-23T15:00:36.138780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
-0.320149281535846.4%
 
-0.244633736231540.8%
 
3.550337086567.3%
 
-0.2648176994314.0%
 
-0.260428119870.9%
 
-0.260643632420.3%
 
1.8342099520.3%
 
-0.260594265410.1%
 
ValueCountFrequency (%) 
-0.320149281535846.4%
 
-0.2648176994314.0%
 
-0.260643632420.3%
 
-0.260594265410.1%
 
-0.260428119870.9%
 
-0.244633736231540.8%
 
1.8342099520.3%
 
3.550337086567.3%
 
ValueCountFrequency (%) 
3.550337086567.3%
 
1.8342099520.3%
 
-0.244633736231540.8%
 
-0.260428119870.9%
 
-0.260594265410.1%
 
-0.260643632420.3%
 
-0.2648176994314.0%
 
-0.320149281535846.4%
 

INSTANCE_DATE_day_normalized
Real number (ℝ≥0)

Distinct31
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5648086244
Minimum0.03225806452
Maximum1
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:36.411086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.03225806452
5-th percentile0.09677419355
Q10.2580645161
median0.6129032258
Q30.8387096774
95-th percentile0.935483871
Maximum1
Range0.9677419355
Interquartile range (IQR)0.5806451613

Descriptive statistics

Standard deviation0.2991556079
Coefficient of variation (CV)0.5296583568
Kurtosis-1.437492778
Mean0.5648086244
Median Absolute Deviation (MAD)0.2580645161
Skewness-0.2048474114
Sum436.0322581
Variance0.08949407776
MonotocityNot monotonic
2020-11-23T15:00:36.523715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
0.22580645167910.2%
 
0.8709677419719.2%
 
0.8064516129678.7%
 
0.9032258065486.2%
 
0.935483871405.2%
 
0.1612903226344.4%
 
0.7741935484334.3%
 
0.4193548387293.8%
 
0.6451612903293.8%
 
0.3548387097263.4%
 
1263.4%
 
0.03225806452243.1%
 
0.8387096774243.1%
 
0.1935483871212.7%
 
0.4516129032182.3%
 
0.2903225806182.3%
 
0.2580645161172.2%
 
0.3870967742172.2%
 
0.7419354839151.9%
 
0.5483870968151.9%
 
0.4838709677151.9%
 
0.5806451613141.8%
 
0.3225806452131.7%
 
0.6129032258131.7%
 
0.7096774194131.7%
 
Other values (6)536.9%
 
ValueCountFrequency (%) 
0.03225806452243.1%
 
0.0645161290391.2%
 
0.09677419355101.3%
 
0.1290322581111.4%
 
0.1612903226344.4%
 
0.1935483871212.7%
 
0.22580645167910.2%
 
0.2580645161172.2%
 
0.2903225806182.3%
 
0.3225806452131.7%
 
ValueCountFrequency (%) 
1263.4%
 
0.967741935560.8%
 
0.935483871405.2%
 
0.9032258065486.2%
 
0.8709677419719.2%
 
0.8387096774243.1%
 
0.8064516129678.7%
 
0.7741935484334.3%
 
0.7419354839151.9%
 
0.7096774194131.7%
 

INSTANCE_DATE_year_after_min_year
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.351036269
Minimum0
Maximum5
Zeros55
Zeros (%)7.1%
Memory size6.2 KiB
2020-11-23T15:00:36.615860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.475203242
Coefficient of variation (CV)0.627469368
Kurtosis-1.331067433
Mean2.351036269
Median Absolute Deviation (MAD)1
Skewness0.1071890667
Sum1815
Variance2.176224606
MonotocityNot monotonic
2020-11-23T15:00:36.697784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
127535.6%
 
418223.6%
 
316321.1%
 
0557.1%
 
2547.0%
 
5435.6%
 
ValueCountFrequency (%) 
0557.1%
 
127535.6%
 
2547.0%
 
316321.1%
 
418223.6%
 
5435.6%
 
ValueCountFrequency (%) 
5435.6%
 
418223.6%
 
316321.1%
 
2547.0%
 
127535.6%
 
0557.1%
 

tree_3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.113989637
Minimum4
Maximum11
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:36.776975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median5
Q35
95-th percentile9
Maximum11
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.338819249
Coefficient of variation (CV)0.2617954559
Kurtosis8.309939337
Mean5.113989637
Median Absolute Deviation (MAD)0
Skewness2.895972495
Sum3948
Variance1.79243698
MonotocityNot monotonic
2020-11-23T15:00:36.857922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
553469.2%
 
417322.4%
 
10263.4%
 
9192.5%
 
6131.7%
 
1170.9%
 
ValueCountFrequency (%) 
417322.4%
 
553469.2%
 
6131.7%
 
9192.5%
 
10263.4%
 
1170.9%
 
ValueCountFrequency (%) 
1170.9%
 
10263.4%
 
9192.5%
 
6131.7%
 
553469.2%
 
417322.4%
 

PROCEDURE_AREA
Real number (ℝ)

SKEWED

Distinct477
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.973823411e-17
Minimum-0.101700452
Maximum27.58029489
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:36.965869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.101700452
5-th percentile-0.101700452
Q1-0.0647660388
median-0.0519032448
Q3-0.03522417304
95-th percentile0.1234748431
Maximum27.58029489
Range27.68199534
Interquartile range (IQR)0.02954186576

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1.114352215e+16
Kurtosis753.3754654
Mean8.973823411e-17
Median Absolute Deviation (MAD)0.0134300181
Skewness27.2987483
Sum6.927791674e-14
Variance1
MonotocityNot monotonic
2020-11-23T15:00:37.082561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.10170045211314.6%
 
-0.09306016063364.7%
 
-0.09717458509202.6%
 
-0.0647660388202.6%
 
-0.06252993856182.3%
 
-0.05805773806121.6%
 
-0.0558216378170.9%
 
-0.0540327576170.9%
 
-0.0459827967150.6%
 
-0.0432994764140.5%
 
0.245372121440.5%
 
-0.0565670045630.4%
 
-0.0535855375630.4%
 
-0.0441059632330.4%
 
-0.0517683334230.4%
 
-0.0310068879720.3%
 
0.204435088820.3%
 
-0.0614118884320.3%
 
-0.0320369848220.3%
 
-0.0232804162420.3%
 
-0.053979091220.3%
 
0.0197853838320.3%
 
0.131000065820.3%
 
-0.0591310661820.3%
 
-0.0575031851920.3%
 
Other values (452)49464.0%
 
ValueCountFrequency (%) 
-0.10170045211314.6%
 
-0.0972252700310.1%
 
-0.09717458509202.6%
 
-0.09306016063364.7%
 
-0.0841664445710.1%
 
-0.066550446810.1%
 
-0.0654234522810.1%
 
-0.0651029445820.3%
 
-0.0650776021110.1%
 
-0.0650582225710.1%
 
ValueCountFrequency (%) 
27.5802948910.1%
 
2.35532501110.1%
 
0.385058322510.1%
 
0.284594810510.1%
 
0.272774784610.1%
 
0.245372121440.5%
 
0.241579695410.1%
 
0.236997180610.1%
 
0.231190773610.1%
 
0.219992383610.1%
 

CREATION_year_after_min_year
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.784974093
Minimum0
Maximum7
Zeros32
Zeros (%)4.1%
Memory size6.2 KiB
2020-11-23T15:00:37.184764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.21293595
Coefficient of variation (CV)0.4355286294
Kurtosis0.5749608199
Mean2.784974093
Median Absolute Deviation (MAD)1
Skewness-0.09371983771
Sum2150
Variance1.471213618
MonotocityNot monotonic
2020-11-23T15:00:37.265283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
331640.9%
 
417322.4%
 
213918.0%
 
18811.4%
 
0324.1%
 
6222.8%
 
710.1%
 
510.1%
 
ValueCountFrequency (%) 
0324.1%
 
18811.4%
 
213918.0%
 
331640.9%
 
417322.4%
 
510.1%
 
6222.8%
 
710.1%
 
ValueCountFrequency (%) 
710.1%
 
6222.8%
 
510.1%
 
417322.4%
 
331640.9%
 
213918.0%
 
18811.4%
 
0324.1%
 

PROCEDURE_NUMBER
Real number (ℝ)

Distinct740
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.363137158e-17
Minimum-1.544780129
Maximum6.054670009
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:37.373519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1.544780129
5-th percentile-1.321203592
Q1-0.6531152261
median-0.05516660067
Q30.3007901373
95-th percentile1.538849492
Maximum6.054670009
Range7.599450138
Interquartile range (IQR)0.9539053635

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1.358116763e+16
Kurtosis7.729789287
Mean7.363137158e-17
Median Absolute Deviation (MAD)0.3942303412
Skewness1.915247877
Sum5.684341886e-14
Variance1
MonotocityNot monotonic
2020-11-23T15:00:37.491411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0438801041630.4%
 
-1.10017047730.4%
 
0.334906224520.3%
 
-0.128289970420.3%
 
-0.0974754400220.3%
 
0.251266784920.3%
 
-0.761210642320.3%
 
0.250777665420.3%
 
-1.21266796920.3%
 
-0.0436722917220.3%
 
0.263005653620.3%
 
-0.23491802820.3%
 
0.25322326320.3%
 
-0.140517958720.3%
 
-0.129268209520.3%
 
-0.631104847320.3%
 
-1.1794078420.3%
 
0.0507277775820.3%
 
-0.631593966820.3%
 
-0.149811229720.3%
 
-0.18698431420.3%
 
0.333927985520.3%
 
0.0497495385220.3%
 
-0.127800850920.3%
 
-0.0676391486920.3%
 
Other values (715)72093.3%
 
ValueCountFrequency (%) 
-1.54478012910.1%
 
-1.54282365110.1%
 
-1.53744333610.1%
 
-1.5335303810.1%
 
-1.51005264310.1%
 
-1.50907440410.1%
 
-1.50858528410.1%
 
-1.50369408910.1%
 
-1.47874899310.1%
 
-1.46701012410.1%
 
ValueCountFrequency (%) 
6.05467000910.1%
 
5.99744302410.1%
 
5.98668239410.1%
 
5.45158562810.1%
 
4.44791235310.1%
 
4.36622939110.1%
 
4.1074851610.1%
 
3.99547678710.1%
 
3.97444464810.1%
 
3.94460835610.1%
 

MUNC_NUMBER
Real number (ℝ)

Distinct459
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1869287122
Minimum-2.044785387
Maximum2.485572941
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:37.618685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.044785387
5-th percentile-1.833656426
Q1-0.1688459216
median0.6313914877
Q30.7140836641
95-th percentile0.8119771257
Maximum2.485572941
Range4.530358328
Interquartile range (IQR)0.8829295857

Descriptive statistics

Standard deviation0.8168755578
Coefficient of variation (CV)4.369984408
Kurtosis1.062818206
Mean0.1869287122
Median Absolute Deviation (MAD)0.1606925981
Skewness-1.388334826
Sum144.3089658
Variance0.6672856769
MonotocityNot monotonic
2020-11-23T15:00:37.741786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.631391487717422.5%
 
0.3304154243131.7%
 
-0.3569266377101.3%
 
0.764168145460.8%
 
0.25734134560.8%
 
0.39785939860.8%
 
0.81577744740.5%
 
0.356806544540.5%
 
0.696958759540.5%
 
-1.83365642640.5%
 
0.454043160430.4%
 
-0.1941227530.4%
 
0.37662920830.4%
 
0.453104809530.4%
 
0.284201640630.4%
 
0.776132119930.4%
 
0.778243409530.4%
 
-1.69806471630.4%
 
0.574621255930.4%
 
-0.0376527310730.4%
 
0.710682141920.3%
 
0.188020669520.3%
 
-0.778011621120.3%
 
0.392346586220.3%
 
0.652504383820.3%
 
Other values (434)50164.9%
 
ValueCountFrequency (%) 
-2.04478538710.1%
 
-2.04431621210.1%
 
-2.04384703610.1%
 
-2.04361244910.1%
 
-2.04337786110.1%
 
-2.04290868510.1%
 
-2.04197033520.3%
 
-1.9171696610.1%
 
-1.91529295810.1%
 
-1.91482378210.1%
 
ValueCountFrequency (%) 
2.48557294110.1%
 
2.46727509810.1%
 
1.1291866610.1%
 
1.09728272810.1%
 
1.09540602710.1%
 
0.827037658210.1%
 
0.826568482820.3%
 
0.825160956410.1%
 
0.824691780910.1%
 
0.824222605410.1%
 

MUNC_ZIP_CODE
Real number (ℝ)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.602632822e-15
Minimum-2.092823108
Maximum1.464762274
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:37.844874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.092823108
5-th percentile-2.092823108
Q1-0.1865599311
median-0.1865599311
Q30.01043991086
95-th percentile1.464762274
Maximum1.464762274
Range3.557585381
Interquartile range (IQR)0.196999842

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-6.239732434e+14
Kurtosis0.1108089394
Mean-1.602632822e-15
Median Absolute Deviation (MAD)0.196999842
Skewness-0.3258195182
Sum-1.237232539e-12
Variance1
MonotocityNot monotonic
2020-11-23T15:00:37.927540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-0.186559931131540.8%
 
0.0104399108618624.1%
 
1.46476227417122.2%
 
-2.092823108567.3%
 
-1.918999718314.0%
 
-1.83208802391.2%
 
-0.82391236120.3%
 
-0.261883400110.1%
 
1.43579170910.1%
 
ValueCountFrequency (%) 
-2.092823108567.3%
 
-1.918999718314.0%
 
-1.83208802391.2%
 
-0.82391236120.3%
 
-0.261883400110.1%
 
-0.186559931131540.8%
 
0.0104399108618624.1%
 
1.43579170910.1%
 
1.46476227417122.2%
 
ValueCountFrequency (%) 
1.46476227417122.2%
 
1.43579170910.1%
 
0.0104399108618624.1%
 
-0.186559931131540.8%
 
-0.261883400110.1%
 
-0.82391236120.3%
 
-1.83208802391.2%
 
-1.918999718314.0%
 
-2.092823108567.3%
 

ACTUAL_WORTH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct501
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5868378.021
Minimum1235000
Maximum105027384
Zeros0
Zeros (%)0.0%
Memory size6.2 KiB
2020-11-23T15:00:38.040295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1235000
5-th percentile1300000
Q12981250
median3868388
Q34770888
95-th percentile28122560
Maximum105027384
Range103792384
Interquartile range (IQR)1789638

Descriptive statistics

Standard deviation9268864.308
Coefficient of variation (CV)1.579459312
Kurtosis28.95516968
Mean5868378.021
Median Absolute Deviation (MAD)903500
Skewness4.691892838
Sum4530387832
Variance8.591184557e+13
MonotocityNot monotonic
2020-11-23T15:00:38.166951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1300000476.1%
 
1274000263.4%
 
1500000192.5%
 
1400000131.7%
 
1372000121.6%
 
1470000101.3%
 
3803888101.3%
 
347688881.0%
 
145000060.8%
 
135000050.6%
 
123500050.6%
 
471888840.5%
 
400000040.5%
 
156800040.5%
 
407188840.5%
 
126100040.5%
 
280000040.5%
 
4110000040.5%
 
477788830.4%
 
350588830.4%
 
370000030.4%
 
348688830.4%
 
352488830.4%
 
255000030.4%
 
1800000030.4%
 
Other values (476)56272.8%
 
ValueCountFrequency (%) 
123500050.6%
 
126100040.5%
 
126670310.1%
 
1274000263.4%
 
128700010.1%
 
1300000476.1%
 
133000010.1%
 
133650010.1%
 
135000050.6%
 
135800010.1%
 
ValueCountFrequency (%) 
10502738410.1%
 
7271000010.1%
 
7200000010.1%
 
5253995510.1%
 
5110000010.1%
 
4938290010.1%
 
4280000010.1%
 
4110000040.5%
 
4001762010.1%
 
3970886010.1%
 

AREA_NAME_EN
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Hadaeq Sheikh Mohammed Bin Rashid
315 
Wadi Al Safa 7
186 
Al Yufrah 2
171 
Island 2
56 
Jumeirah First
 
31
ValueCountFrequency (%) 
Hadaeq Sheikh Mohammed Bin Rashid31540.8%
 
Wadi Al Safa 718624.1%
 
Al Yufrah 217122.2%
 
Island 2567.3%
 
Jumeirah First314.0%
 
Rare cases131.7%
 
2020-11-23T15:00:38.283001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-23T15:00:38.351779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:38.453246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length14
Mean length20.58549223
Min length8

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
226014.2%
 
a210213.2%
 
h14629.2%
 
i11937.5%
 
d11877.5%
 
e10026.3%
 
m6614.2%
 
S5013.2%
 
s4282.7%
 
l4132.6%
 
n3712.3%
 
A3572.2%
 
f3572.2%
 
R3282.1%
 
H3152.0%
 
q3152.0%
 
k3152.0%
 
M3152.0%
 
o3152.0%
 
B3152.0%
 
r2461.5%
 
22271.4%
 
u2021.3%
 
W1861.2%
 
71861.2%
 
Other values (6)3332.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1061366.8%
 
Uppercase Letter260616.4%
 
Space Separator226014.2%
 
Decimal Number4132.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S50119.2%
 
A35713.7%
 
R32812.6%
 
H31512.1%
 
M31512.1%
 
B31512.1%
 
W1867.1%
 
Y1716.6%
 
I562.1%
 
J311.2%
 
F311.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a210219.8%
 
h146213.8%
 
i119311.2%
 
d118711.2%
 
e10029.4%
 
m6616.2%
 
s4284.0%
 
l4133.9%
 
n3713.5%
 
f3573.4%
 
q3153.0%
 
k3153.0%
 
o3153.0%
 
r2462.3%
 
u2021.9%
 
t310.3%
 
c130.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2260100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
222755.0%
 
718645.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1321983.2%
 
Common267316.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a210215.9%
 
h146211.1%
 
i11939.0%
 
d11879.0%
 
e10027.6%
 
m6615.0%
 
S5013.8%
 
s4283.2%
 
l4133.1%
 
n3712.8%
 
A3572.7%
 
f3572.7%
 
R3282.5%
 
H3152.4%
 
q3152.4%
 
k3152.4%
 
M3152.4%
 
o3152.4%
 
B3152.4%
 
r2461.9%
 
u2021.5%
 
W1861.4%
 
Y1711.3%
 
I560.4%
 
J310.2%
 
Other values (3)750.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
226084.5%
 
22278.5%
 
71867.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15892100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
226014.2%
 
a210213.2%
 
h14629.2%
 
i11937.5%
 
d11877.5%
 
e10026.3%
 
m6614.2%
 
S5013.2%
 
s4282.7%
 
l4132.6%
 
n3712.3%
 
A3572.2%
 
f3572.2%
 
R3282.1%
 
H3152.0%
 
q3152.0%
 
k3152.0%
 
M3152.0%
 
o3152.0%
 
B3152.0%
 
r2461.5%
 
22271.4%
 
u2021.3%
 
W1861.2%
 
71861.2%
 
Other values (6)3332.1%
 

STATUS_CODE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
VACANT
672 
PREMISED
100 
ValueCountFrequency (%) 
VACANT67287.0%
 
PREMISED10013.0%
 
2020-11-23T15:00:38.546646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-23T15:00:38.608252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:38.682463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length6.259067358
Min length6

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A134427.8%
 
V67213.9%
 
C67213.9%
 
N67213.9%
 
T67213.9%
 
E2004.1%
 
P1002.1%
 
R1002.1%
 
M1002.1%
 
I1002.1%
 
S1002.1%
 
D1002.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter4832100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A134427.8%
 
V67213.9%
 
C67213.9%
 
N67213.9%
 
T67213.9%
 
E2004.1%
 
P1002.1%
 
R1002.1%
 
M1002.1%
 
I1002.1%
 
S1002.1%
 
D1002.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4832100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A134427.8%
 
V67213.9%
 
C67213.9%
 
N67213.9%
 
T67213.9%
 
E2004.1%
 
P1002.1%
 
R1002.1%
 
M1002.1%
 
I1002.1%
 
S1002.1%
 
D1002.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4832100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A134427.8%
 
V67213.9%
 
C67213.9%
 
N67213.9%
 
T67213.9%
 
E2004.1%
 
P1002.1%
 
R1002.1%
 
M1002.1%
 
I1002.1%
 
S1002.1%
 
D1002.1%
 
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Rare cases
728 
Unknown
 
44
ValueCountFrequency (%) 
Rare cases72894.3%
 
Unknown445.7%
 
2020-11-23T15:00:38.788735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-23T15:00:38.850548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:38.930416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.829015544
Min length7

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a145619.2%
 
e145619.2%
 
s145619.2%
 
R7289.6%
 
r7289.6%
 
7289.6%
 
c7289.6%
 
n1321.7%
 
U440.6%
 
k440.6%
 
o440.6%
 
w440.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter608880.2%
 
Uppercase Letter77210.2%
 
Space Separator7289.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R72894.3%
 
U445.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a145623.9%
 
e145623.9%
 
s145623.9%
 
r72812.0%
 
c72812.0%
 
n1322.2%
 
k440.7%
 
o440.7%
 
w440.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
728100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin686090.4%
 
Common7289.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a145621.2%
 
e145621.2%
 
s145621.2%
 
R72810.6%
 
r72810.6%
 
c72810.6%
 
n1321.9%
 
U440.6%
 
k440.6%
 
o440.6%
 
w440.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
728100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7588100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a145619.2%
 
e145619.2%
 
s145619.2%
 
R7289.6%
 
r7289.6%
 
7289.6%
 
c7289.6%
 
n1321.7%
 
U440.6%
 
k440.6%
 
o440.6%
 
w440.6%
 

PROJECT_NAME_EN
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
SIDRA
319 
AQUILEGIA @ AKOYA OXYGEN
171 
ARABIAN RANCHES- AZALEA COMMUNITY
105 
ARABIAN RANCHES - SAMARA COMMUNITY
81 
Unknown
78 
ValueCountFrequency (%) 
SIDRA31941.3%
 
AQUILEGIA @ AKOYA OXYGEN17122.2%
 
ARABIAN RANCHES- AZALEA COMMUNITY10513.6%
 
ARABIAN RANCHES - SAMARA COMMUNITY8110.5%
 
Unknown7810.1%
 
BV Mansions 182.3%
 
2020-11-23T15:00:39.030627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-23T15:00:39.097066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:39.204294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length7
Mean length16.42487047
Min length5

Overview of Unicode Properties

Unique unicode characters32
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A230518.2%
 
11889.4%
 
I10338.1%
 
R7726.1%
 
N7295.7%
 
E6335.0%
 
S5864.6%
 
O5284.2%
 
Y5284.2%
 
M4713.7%
 
U4353.4%
 
C3722.9%
 
G3422.7%
 
D3192.5%
 
L2762.2%
 
n2702.1%
 
B2041.6%
 
H1861.5%
 
-1861.5%
 
T1861.5%
 
Q1711.3%
 
@1711.3%
 
K1711.3%
 
X1711.3%
 
Z1050.8%
 
Other values (7)3422.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1054183.1%
 
Space Separator11889.4%
 
Lowercase Letter5944.7%
 
Dash Punctuation1861.5%
 
Other Punctuation1711.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A230521.9%
 
I10339.8%
 
R7727.3%
 
N7296.9%
 
E6336.0%
 
S5865.6%
 
O5285.0%
 
Y5285.0%
 
M4714.5%
 
U4354.1%
 
C3723.5%
 
G3423.2%
 
D3193.0%
 
L2762.6%
 
B2041.9%
 
H1861.8%
 
T1861.8%
 
Q1711.6%
 
K1711.6%
 
X1711.6%
 
Z1051.0%
 
V180.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1188100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
@171100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n27045.5%
 
o9616.2%
 
k7813.1%
 
w7813.1%
 
s366.1%
 
a183.0%
 
i183.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-186100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1113587.8%
 
Common154512.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A230520.7%
 
I10339.3%
 
R7726.9%
 
N7296.5%
 
E6335.7%
 
S5865.3%
 
O5284.7%
 
Y5284.7%
 
M4714.2%
 
U4353.9%
 
C3723.3%
 
G3423.1%
 
D3192.9%
 
L2762.5%
 
n2702.4%
 
B2041.8%
 
H1861.7%
 
T1861.7%
 
Q1711.5%
 
K1711.5%
 
X1711.5%
 
Z1050.9%
 
o960.9%
 
k780.7%
 
w780.7%
 
Other values (4)900.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
118876.9%
 
-18612.0%
 
@17111.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12680100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A230518.2%
 
11889.4%
 
I10338.1%
 
R7726.1%
 
N7295.7%
 
E6335.0%
 
S5864.6%
 
O5284.2%
 
Y5284.2%
 
M4713.7%
 
U4353.4%
 
C3722.9%
 
G3422.7%
 
D3192.5%
 
L2762.2%
 
n2702.1%
 
B2041.6%
 
H1861.5%
 
-1861.5%
 
T1861.5%
 
Q1711.3%
 
@1711.3%
 
K1711.3%
 
X1711.3%
 
Z1050.8%
 
Other values (7)3422.7%
 

MASTER_PROJECT_EN
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Unknown
415 
558 Villa
186 
Dubai Tiger Woods
171 
ValueCountFrequency (%) 
Unknown41553.8%
 
558 Villa18624.1%
 
Dubai Tiger Woods17122.2%
 
2020-11-23T15:00:39.306631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-23T15:00:39.369822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:39.444046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length7
Mean length9.696891192
Min length7

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n124516.6%
 
o75710.1%
 
i5287.1%
 
5287.1%
 
U4155.5%
 
k4155.5%
 
w4155.5%
 
53725.0%
 
l3725.0%
 
a3574.8%
 
81862.5%
 
V1862.5%
 
D1712.3%
 
u1712.3%
 
b1712.3%
 
T1712.3%
 
g1712.3%
 
e1712.3%
 
r1712.3%
 
W1712.3%
 
d1712.3%
 
s1712.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter528670.6%
 
Uppercase Letter111414.9%
 
Decimal Number5587.5%
 
Space Separator5287.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U41537.3%
 
V18616.7%
 
D17115.4%
 
T17115.4%
 
W17115.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n124523.6%
 
o75714.3%
 
i52810.0%
 
k4157.9%
 
w4157.9%
 
l3727.0%
 
a3576.8%
 
u1713.2%
 
b1713.2%
 
g1713.2%
 
e1713.2%
 
r1713.2%
 
d1713.2%
 
s1713.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
528100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
537266.7%
 
818633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin640085.5%
 
Common108614.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n124519.5%
 
o75711.8%
 
i5288.2%
 
U4156.5%
 
k4156.5%
 
w4156.5%
 
l3725.8%
 
a3575.6%
 
V1862.9%
 
D1712.7%
 
u1712.7%
 
b1712.7%
 
T1712.7%
 
g1712.7%
 
e1712.7%
 
r1712.7%
 
W1712.7%
 
d1712.7%
 
s1712.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
52848.6%
 
537234.3%
 
818617.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7486100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n124516.6%
 
o75710.1%
 
i5287.1%
 
5287.1%
 
U4155.5%
 
k4155.5%
 
w4155.5%
 
53725.0%
 
l3725.0%
 
a3574.8%
 
81862.5%
 
V1862.5%
 
D1712.3%
 
u1712.3%
 
b1712.3%
 
T1712.3%
 
g1712.3%
 
e1712.3%
 
r1712.3%
 
W1712.3%
 
d1712.3%
 
s1712.3%
 

Interactions

2020-11-23T15:00:22.044716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.182691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.302744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.416405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.529103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.644231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.755631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:22.870389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.085666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.210765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.324150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.433127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.550236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.667831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.779283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:23.888732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.000764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.108532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.221958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.332236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.437242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.548711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.658670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.768343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.876839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:24.978955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.078533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.181726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.280122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.383817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.486836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.582313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.685975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.785965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:25.894543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.001806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.106503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.205041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.305086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.402336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.504648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.711792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.825193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:26.929101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.029660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.139604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.249109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.352795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.455449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.564585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.667117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.774131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.886628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:27.986055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.093612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.197317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.300625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.404201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.501831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.599019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.698198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.795672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.897466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:28.997585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.093844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.196225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.293334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.406397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.518334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.624073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.730606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.844662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:29.950346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.062859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.171555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.273313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.383452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.490834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.603224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.713928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:30.818648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.053410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.180774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.284563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.392494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.500299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.601340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.709885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.814282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:31.916359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.018398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.115194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.210132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.307830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.401939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.501812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.600402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.699628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.801217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:32.898580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.013898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.127962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.235215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.341943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.451513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.554688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.662339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.770770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.872104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:33.979779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.084752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.192852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.302184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.404714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.504993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.606658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.705649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.812583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:34.915875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:35.011382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:35.115266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-11-23T15:00:39.534912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-23T15:00:39.710462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-23T15:00:39.885448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-23T15:00:40.074105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-23T15:00:40.253534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-23T15:00:35.327127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-23T15:00:35.672759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

CURRENT_STATUS_year_after_min_yearSEPARATED_REFERENCEINSTANCE_DATE_day_normalizedINSTANCE_DATE_year_after_min_yeartree_3PROCEDURE_AREACREATION_year_after_min_yearPROCEDURE_NUMBERMUNC_NUMBERMUNC_ZIP_CODEACTUAL_WORTHAREA_NAME_ENSTATUS_CODEPRE_REGISTRATION_NUMBERPROJECT_NAME_ENMASTER_PROJECT_EN
00.0-0.3201490.03225834-0.1017004-0.9866950.6313911.4647621300000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
10.0-0.3201490.93548434-0.0930604-1.1764730.6313911.4647621568000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
20.0-0.3201490.77419434-0.1017004-0.7988730.6313911.4647621300000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
30.0-0.3201490.19354844-0.09306040.2532230.6313911.4647621450000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
47.0-0.2446340.83871015-0.03294030.2713210.675025-0.1865604891888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown
57.0-0.2446340.22580615-0.0580533-0.1282900.697545-0.1865603503888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown
67.0-0.2446340.87096815-0.03980830.2879510.808740-0.1865604707888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown
70.0-0.3201490.16129034-0.10170040.0296960.6313911.4647621274000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
86.0-0.2648180.806452150.03959005.997443-0.664002-1.9190006047337.0Jumeirah FirstVACANTUnknownUnknownUnknown
97.0-0.2446340.03225815-0.0349533-0.381165-0.725933-0.1865605043888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown

Last rows

CURRENT_STATUS_year_after_min_yearSEPARATED_REFERENCEINSTANCE_DATE_day_normalizedINSTANCE_DATE_year_after_min_yeartree_3PROCEDURE_AREACREATION_year_after_min_yearPROCEDURE_NUMBERMUNC_NUMBERMUNC_ZIP_CODEACTUAL_WORTHAREA_NAME_ENSTATUS_CODEPRE_REGISTRATION_NUMBERPROJECT_NAME_ENMASTER_PROJECT_EN
7627.00-0.2446340.80645215-0.03970130.2414840.809678-0.1865604097888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown
7637.00-0.2446340.22580615-0.0278703-0.1595940.772613-0.1865605182888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown
7647.00-0.3201490.54838725-0.00662711.4266210.0201730.0104403901888.0Wadi Al Safa 7PREMISEDRare casesARABIAN RANCHES - SAMARA COMMUNITY558 Villa
7650.00-0.3201490.87096834-0.0971754-0.8927840.6313911.4647621500000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
7667.00-0.3201490.16129025-0.05582220.912067-0.3304180.0104404113888.0Wadi Al Safa 7VACANTRare casesARABIAN RANCHES- AZALEA COMMUNITY558 Villa
7670.00-0.3201490.93548434-0.0930604-1.1701150.6313911.4647621372000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
7687.75-0.3201490.58064505-0.05913110.9223390.5383770.0104403069799.0Wadi Al Safa 7PREMISEDRare casesARABIAN RANCHES - SAMARA COMMUNITY558 Villa
7696.00-0.2648180.967742150.01871800.2727880.028970-1.9190005941806.0Jumeirah FirstVACANTUnknownUnknownUnknown
7700.00-0.3201490.80645234-0.0971754-0.0720410.6313911.4647621500000.0Al Yufrah 2VACANTRare casesAQUILEGIA @ AKOYA OXYGENDubai Tiger Woods
7717.00-0.2446340.03225815-0.0459833-0.387034-0.736724-0.1865604580888.0Hadaeq Sheikh Mohammed Bin RashidVACANTRare casesSIDRAUnknown